Pre-programme Assignment 1
Cheik Samassa
20/10/2020
Task1: a short biography
My name is Cheik Samassa I am 22, from Paris, France. I have a bachelor in Demography and a Master’s degree in Big data and social Analysis. I am already familiar with R which I previously used for:
- statistical modelling - survival analysis - cartography. Neertheless, I am more confortable (and prefer) using Python, especially the Pandas library. Despite having been through a significant amount of statistics courses, I don’t consider myself being “good in stats” and I am thus looking forward to this course which will help me strengthen and deepen my statistics skills and hopefully become “good in stats”. Ps: I really like the fact that we have to go through introductory courses to R before using it to deep dive into the statistical concepts. My previous statistics teachers would just start explaining things from a mathematical perspective and then jump into the coding part using R without even making sure that everyone is already capable of using/understanding R
Here is a link to an analysis I made with a classmate on the New York citybikes last year : https://rpubs.com/CheikSama/608619
And here is an image (I spent way too much trying to find a good one, I am satisfied enough with the result though)
Alt Text
Task 2: gapminder country comparison
You have seen the gapminder dataset that has data on life expectancy, population, and GDP per capita for 142 countries from 1952 to 2007. To get a glipmse of the dataframe, namely to see the variable names, variable types, etc., we use the glimpse function. We also want to have a look at the first 20 rows of data.
## Observations: 1,704
## Variables: 6
## $ country <fct> Afghanistan, Afghanistan, Afghanistan, Afghanistan, Afghani…
## $ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia,…
## $ year <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997,…
## $ lifeExp <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.854, 40.…
## $ pop <int> 8425333, 9240934, 10267083, 11537966, 13079460, 14880372, 1…
## $ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.1134,…## # A tibble: 20 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.
## 11 Afghanistan Asia 2002 42.1 25268405 727.
## 12 Afghanistan Asia 2007 43.8 31889923 975.
## 13 Albania Europe 1952 55.2 1282697 1601.
## 14 Albania Europe 1957 59.3 1476505 1942.
## 15 Albania Europe 1962 64.8 1728137 2313.
## 16 Albania Europe 1967 66.2 1984060 2760.
## 17 Albania Europe 1972 67.7 2263554 3313.
## 18 Albania Europe 1977 68.9 2509048 3533.
## 19 Albania Europe 1982 70.4 2780097 3631.
## 20 Albania Europe 1987 72 3075321 3739.Your task is to produce two graphs of how life expectancy has changed over the years for the country and the continent you come from.
I have created the country_data and continent_data with the code below.
country_data <- gapminder %>%
filter(country == "France")
continent_data <- gapminder %>%
filter(continent == "Europe")First, create a plot of life expectancy over time for the single country you chose. You should use geom_point() to see the actual data points and geom_smooth(se = FALSE) to plot the underlying trendlines. You need to remove the comments # from the lines below for your code to run.
ggplot(data = country_data, mapping = aes(x = year, y = lifeExp))+
geom_point() +
geom_smooth(se = T)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Next we need to add a title. Create a new plot, or extend plot1, using the labs() function to add an informative title to the plot.
ggplot(data = country_data, mapping = aes(x = year, y = lifeExp))+
geom_point() +
geom_smooth(se = T)+ggtitle("Evolution of life expectency in France")## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Secondly, produce a plot for all countries in the continent you come from. (Hint: map the country variable to the colour aesthetic).
ggplot(data = continent_data , mapping = aes(x = year , y = lifeExp , colour= country))+
geom_point()+
geom_smooth(se = FALSE) + labs (title="Evolution of life expexctancy in Europe", subtitle="From 1952", x="year",y="life expectancy")## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Finally, using the original gapminder data, produce a life expectancy over time graph, grouped (or faceted) by continent. We will remove all legends, adding the theme(legend.position="none") in the end of our ggplot.
ggplot(data = gapminder , mapping = aes(x = year , y =lifeExp))+ geom_point() +
geom_smooth(se = FALSE) +
facet_wrap(~continent) +
theme(legend.position="none") + #remove all legends
labs (title="Life expectancy over time", subtitle="by continent since 1952",y="life expectancy", x="year") ## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Given these trends, what can you say about life expectancy since 1952? Again, don’t just say what’s happening in the graph. Tell some sort of story and speculate about the differences in the patterns.
Type your answer after this blockquote.
Task 3: Brexit voting
We will have a quick look at the results of the 2016 Brexit vote in the UK. First we read the data using read_csv() and have a quick glimpse at the data
setwd("/Users/cheiksamassa/Library/Mobile Documents/com~apple~CloudDocs")
brexit_results <- read_csv(here::here("Data","brexit_results.csv"))
glimpse(brexit_results)## Observations: 632
## Variables: 11
## $ Seat <chr> "Aldershot", "Aldridge-Brownhills", "Altrincham and Sale …
## $ con_2015 <dbl> 50.592, 52.050, 52.994, 43.979, 60.788, 22.418, 52.454, 2…
## $ lab_2015 <dbl> 18.333, 22.369, 26.686, 34.781, 11.197, 41.022, 18.441, 4…
## $ ld_2015 <dbl> 8.824, 3.367, 8.383, 2.975, 7.192, 14.828, 5.984, 2.423, …
## $ ukip_2015 <dbl> 17.867, 19.624, 8.011, 15.887, 14.438, 21.409, 18.821, 21…
## $ leave_share <dbl> 57.89777, 67.79635, 38.58780, 65.29912, 49.70111, 70.4728…
## $ born_in_uk <dbl> 83.10464, 96.12207, 90.48566, 97.30437, 93.33793, 96.9621…
## $ male <dbl> 49.89896, 48.92951, 48.90621, 49.21657, 48.00189, 49.1718…
## $ unemployed <dbl> 3.637000, 4.553607, 3.039963, 4.261173, 2.468100, 4.74273…
## $ degree <dbl> 13.870661, 9.974114, 28.600135, 9.336294, 18.775591, 6.08…
## $ age_18to24 <dbl> 9.406093, 7.325850, 6.437453, 7.747801, 5.734730, 8.20986…The data comes from Elliott Morris, who cleaned it and made it available through his DataCamp class on analysing election and polling data in R.
Our main outcome variable (or y) is leave_share, which is the percent of votes cast in favour of Brexit, or leaving the EU. Each row is a UK parliament constituency.
To get a sense of the spread of the data, plot a histogram and a density plot of the leave share in all constituencies.
ggplot(brexit_results, aes(x = leave_share)) +
geom_histogram(binwidth = 2.5)+labs (title="Distribution of Brexit votes", subtitle=" by parliament_constituencies",y="number", x="% in in favor") 
One common explanation for the Brexit outcome was fear of immigration and opposition to the EU’s more open border policy. We can check the relationship (or correlation) between the proportion of native born residents (born_in_uk) in a constituency and its leave_share. To do this, let us get the correlation between the two variables
## leave_share born_in_uk
## leave_share 1.0000000 0.4934295
## born_in_uk 0.4934295 1.0000000The correlation is almost 0.5, which shows that the two variables are positively correlated.
We can also create a scatterplot between these two variables using geom_point. We also add the best fit line, using geom_smooth(method = "lm").
ggplot(brexit_results, aes(x = born_in_uk, y = leave_share)) +
geom_point(alpha=0.3) +
geom_smooth(method = "lm") +
theme_bw() + labs (title="Votes in favor of brexit by proportion of native born in residents ", subtitle="by %",y="Brexit_share (%)", x="Proportion of UK born residents (%)") 
You have the code for the plots, I would like you to revisit all of them and use the labs() function to add an informative title, subtitle, and axes titles to all plots.
What can you say about the relationship shown above? Again, don’t just say what’s happening in the graph. Tell some sort of story and speculate about the differences in the patterns.
Type your answer after, and outside, this blockquote.
We can say that the amount of people in favor of Brexit increases with the proportion of UK born residents: Constituents where there is a high number of native born residents are more likely to vote for Brexit than Consituents where there are less UK born residents. We could even speculate that people born in the United Kingdom are more willing to vote for Brexit than the others.
Submit the assignment
Knit the completed R Markdown file as ah HTML or Word document (use the “Knit” button at the top of the script editor window) and upload it to Canvas.
Details
If you want to, please answer the following
- Approximately how much time did you spend on this problem set: 30 minutes
- What, if anything, gave you the most trouble: Finding smart labels for the graphs and a nice image.